The other way to optimize a policy is to use imitation learning [1]} in which trajectory
optimization methods play a role in giving samples, and the neural net
policy is trained to these samples through supervised learning.
The PMP method was used by [2]} to generate trajectory samples to train a neural network policy via supervised learning. This method was validated on the soft-landing problem with a 3-degree-of-freedom (DoF) rocket.
Real-time trajectory generation for asteroid landings was conducted
by imitation learning in [3]}. The downside of imitation learning
is that the errors in supervised learning can be compounded along
the trajectory over a time horizon. Then, this accumulated error can
result in poor propagated trajectories over a long time horizon especially for
higher-dimensional systems.
